Face Recognition Method by Weight Fusion of Original and Mirror Samples

doi:10.3969/j.issn.1006-2475.2017.03.019

Abstract

Abstract:

Focused on the small sample size problem, a method for face recognition based on local binary pattern features of weight fusing the original and mirror image was

proposed. Firstly the proposed scheme generated the mirror images of the original training samples through the geometric transformation. Then each sample was divided into

several blocks, and the concatenated LBP histogram calculated over each block was used as the face feature of the sample. Next, the original and their mirror images face

feature was merged into new samples by weighted fusion scheme. Finally, the test sample was classified into the correct subject by using the histogram intersection method.

Experimental results showed that the algorithm has achieved a higher recognition rate, with the fact the recognition rate on FERET face database increased by 25.17%, 15.8% and

25.17%, respectively, compared with the contrast algorithm when the number of training sample per subject was 1,2 and 3 respectively. 

Key words: face recognition, small sample size, virtual sample, local binary pattern, weight fusion

CLC Number:

TP391.41

CHEN Xiang， WANG Ling. Face Recognition Method by Weight Fusion of Original and Mirror Samples[J]. Computer and Modernization, 2017, 0(3): 91-.

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Face Recognition Method by Weight Fusion of Original and Mirror Samples

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